因此，我正在训练图像分类模型，并且出现此错误。对于此错误，似乎没有任何答案。有人可以解释一下我的代码有什么问题吗？我正在使用tf.data。标签是否有任何问题，我该怎么办才能解决此问题：

import numpy as np
import pandas as pd
import os
from tqdm import tqdm
from sklearn.utils import shuffle

import cv2
import warnings

warnings.filterwarnings('ignore')

import tensorflow as tf
from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Flatten, Dropout, Activation, Conv1D, MaxPool1D

from tensorflow.keras.layers import Dense, Dropout, Activation, Input, BatchNormalization, GlobalAveragePooling2D

physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
training_folder = r"F:\Pycharm_projects\Kaggle Cassava\data\train_images"
samples_df = pd.read_csv(r"F:\Pycharm_projects\Kaggle Cassava\data\train.csv")
samples_df = shuffle(samples_df, random_state=42)
samples_df["label"] = samples_df["label"].astype("str")
samples_df.head()
temp_labels = {}
imgg = []
lab = []
for i in range(len(samples_df)):
    image_name = samples_df.iloc[i, 0]
    image_label = samples_df.iloc[i, 1]
    la = {image_name: image_label}
    temp_labels.update(la)
print(len(temp_labels))
for im in tqdm(os.listdir(training_folder)):
    path = os.path.join(training_folder, im)
    label = temp_labels.get(im)
    img = cv2.imread(path)
    img = tf.image.random_crop(img, size=(150, 150, 3))
    imgg.append(img)
    lab.append(label)

lables = np.array(lab).astype(np.float32)
img = np.array(imgg).astype(np.float32)
train = tf.data.Dataset.from_tensor_slices((img, lables)).shuffle(buffer_size=1000)
print(tf.data.Dataset.cardinality(train))
model = Sequential()
model.add(Conv1D(filters=16, kernel_size=2, strides=1, activation="relu"))
model.add(BatchNormalization())

model.add(Conv1D(filters=16, kernel_size=2, strides=1, activation="relu"))
model.add(BatchNormalization())

model.add(BatchNormalization())

model.add(Flatten())
model.add(Dense(5, activation="sigmoid"))

tf.keras.optimizers.Adam(
    learning_rate=0.0001, )
model.compile(optimizer='adam',
              loss="categorical_crossentropy"
              ,
              metrics=['accuracy'])
model.fit(train, batch_size=32, shuffle=True, epochs=1)

我该怎么办才能解决此错误。